End-to-End Testing: Ensuring the Quality of Software Applications; Artificial Intelligence’s Effect on the Workforce: Is Your Job at Risk?

The rapid advancement of technology in the workforce has brought about higher levels of efficiency, innovation, and productivity. However, as technologies, such as artificial intelligence (AI), continue to evolve and integrate into various industries, there is a growing concern regarding the displacement of human labor and the potential for job loss. This issue is important as it may directly affect individuals’ livelihoods, socio-economic stability, and the overall distribution of wealth and opportunities in society. Additionally, as AI continues to grow, the need for quality software testing does as well. My STS research delves into the multifaceted effects of AI on employment, highlighting both the potential benefits and drawbacks. It also discusses strategies and plans that should be implemented for a smooth transition to an AI-driven workforce. My technical paper on building an automated, regression testing suite for a fintech firm intersects with this problem by exemplifying the practical applications of AI in streamlining processes and enhancing efficiency, which can inadvertently contribute to job automation and workforce restructuring.
Software testing is a crucial part of any software development cycle by ensuring the validation and verification of the intended program. Without quality assurance, serious consequences may arise from highly scaled, intricate software. My technical project involved the development of an automated, regression testing suite for specific front-end components of a fintech firm’s web application. Since this company is a banking firm, it is crucial that their application displays accurate information as confidential, personal information is involved. Incorrectly presented HTML components can lead to disasters, such as customer data leaks. To create this testing pipeline, my team and I deployed a combination of Cypress tests and Python scripts to verify data flow. These different components were dictated by a master script with the final results stored into the company Confluence page. The finished product yielded a reliable testing suite and offered an overall decrease in testing time as well as strict quality control.
Artificial intelligence (AI) has been rapidly integrated into the workforce to promote automation in various industries such as manufacturing and automotive. Through the literary analysis of various sources, my STS research explores the multi-faceted implications of AI on employment and discusses the strategies that will help ensure an ethical, AI-driven future. As the capabilities of AI technologies continue to evolve, issues such as job displacement, socio-economic inequality, and changes in labor market dynamics arise and challenge the security of society. Conversely, the potential of creating new employment opportunities, enhancing skill sets, and driving educational transformations could catalyze changes within technically advanced sectors. Thus, given the uncertainty surrounding the future impact of AI, it is important to establish preemptive plans to mitigate adverse outcomes and maximize the benefits of AI implementation. Key stakeholders, including policymakers, corporate executives, and employees, are integral to this effort and should convene to discuss essential strategies required across diverse sectors, such as education.
In contributing to the solution of the overarching problem regarding the rapid evolution of technologies in the workforce, I made strong progress in both social and technical aspects. While I was successful in developing an automated testing suite to enhance software quality at a financial institution, the resulting product could be expanded to test other parts of the web application. One limitation of my research is the ongoing evolution of AI technologies, making it challenging to accurately predict their full extent of impact on employment. Its rapidly evolving, complex nature also makes it difficult to anticipate its long term effects. Future research includes studying recent AI technologies and analyzing how they may possibly affect jobs within the next decade. With this new information, future researchers can help develop appropriate strategies and advocate for their implementation to policymakers.
I would like to thank Caitlyn Wylie, Alice Fox, and Rosanne Vrugtman for assisting me in building my thesis work by providing me with feedback, information, and advice when completing this report. I would also like to thank my team members and managers at Capital One for guiding me through the completion of my software testing project.

School of Engineering and Applied Science

Bachelor of Science in Computer Science

Technical Advisor: Rosanne Vrugtman

STS Advisor: Caitlin Wylie

Technical Team Members: n/a